Fast Cross-Validation via Sequential Analysis
Tammo Krüger, Danny Panknin and Mikio braun
In: Big Learning: Algorithms, Systems, and Tools for Learning at Scale, 16 Dec 2011, Granda, Spain.
With the increasing size of today's data sets, finding the right parameter configuration via cross-validation can be an extremely time-consuming task. In this paper we propose an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating underperforming candidates quickly and keeping promising candidates as long as possible the method speeds up the computation while preserving the capability of the full cross-validation. The experimental evaluation shows that our method reduces the computation time by a factor of up to 70 compared to a full cross-validation with a negligible impact on the accuracy.
|EPrint Type:||Conference or Workshop Item (Talk)|
|Project Keyword:||Project Keyword UNSPECIFIED|
|Subjects:||Theory & Algorithms|
|Deposited By:||Mikio braun|
|Deposited On:||16 March 2012|